Lab exercise 9, due right before the next class
GEO5083: Remote Sensing Image Processing and Analysis, UTSA

Student name: ______________

Assessment of
image
classification accuracy

Purpose

This lab is to help you understand how ENVI can be used to do accuracy
assessment based on confusion matrix or error matrix.

Step 1.
Select ground truth sites via ROI

Based on
the last lab, you used the same ROIs (training sites) for three supervised
classification methods to classify your image. now you will select ground truth
sites via ROI tool for each class you classified. You should not selected the
same pixels you used as for training sites. The best way to do so is that,
during the time when you select your training ROIs, you also select your ground
truth ROIs. so for each class, you have pairs of ROIs: training ROIs and ground
truth ROIs. the training ROIs will be used for classification while ground truth
ROIs will be used for accuracy assessment.

Step
2. Confusion
matrix

go to ENVI menu, Classification -> Post classification -> Confusion Matrix ->
Using Ground Truth ROIs. A window called Classification Input File is popup for
you to select one class map (for example the SAM classification map). Once you
select the map, a new window called Match Classes Parameters is popup as the
window below:

then you need to select the real match. for example, in the test, my ground
truth Region #4 is the same class of Region #1 (from the training class). So the
Region #4 and #1 should be a pair or Matched Class. The same reason, ground truth Region #5
and training site Region #2 are a pair, and ground truth Region #6 and training
site Region #3 are a pair. Then we do the following match:

Click OK, you will see the following
window:

Click OK, You see the error matrix of
accuracy assessment:

the overall accuracy is 57%,
and Kappa is 0.485. the classification agreement for region 2 and 3 are both
100%, but for region 1 is 0. Omission error for region 1 is 100%, for regions 2
and 3 is 0%.

Using the same ground truth ROIs for
the two other classification maps to product error matrix. then compare them to
see which method has the best performance among the three methods. write a
report.